162
M. Nakao
procedure. Since many existing parallel applications are written in MPI library, these
functions can be useful for extending existing applications. Furthermore, it will be
possible to effectively use the high functionality of Python.
References
1. M. Nakao et al., Linkage of XcalableMP and Python languages for high productivity on HPC
cluster system, in Proceedings of Workshops of HPC Asia, No .9 (2018), pp.39–47
2. F. Cantonnet et al., Productivity analysis of the UPC language, in 18th International Parallel
and Distributed Processing Symposium (2004), pp.254–260
3. K. Yelick et al., Productivity and performance using partitioned global address space languages, in Proceedings of the International Workshop on Parallel Symbolic Computation
(2007)
4. A.I. Stone et al., Evaluating coarray Fortran with the CGPOP Miniapp, in Proceedings of the
Fifth Conference on Partitioned Global Address Space Programming Models (2011)
5. M. Nakao et al., Implementation and evaluation of the HPC challenge benchmark in the
XcalableMP PGAS language. Int. J. High Perform. Comput. Appl. 33, 110–123 (2017)
6. J. Jithin et al., Unifying UPC and MPI runtimes: experience with MVAPICH, in Proceedings
of the Fourth Conference on Partitioned Global Address Space Programming Model (2010),
pp. 5:1–5:10
7. M. Nakao et al., Productivity and performance of the HPC challenge benchmarks with the
XcalableMP PGAS language, in Proceedings of the Fourth Conference on Partitioned Global
Address Space Programming Model (2013), pp. 157–171
8. M. Nakao et al., Productivity and performance of global-view programming with XcalableMP
PGAS language, in Proceedings of the 2012 12th IEEE/ACM International Symposium on
Cluster, Cloud and Grid Computing (2012), pp. 402–409
9. M. Nakao et al., XcalableACC: extension of XcalableMP PGAS language using OpenACC for
accelerator clusters, in Proceedings of the First Workshop on Accelerator Programming Using
Directives (2014), pp.27–36
10. M. Nakao et al., Implementing lattice QCD application with XcalableACC language on
accelerated cluster, in IEEE International Conference on Cluster Computing (CLUSTER)
(2017), pp. 429–438
11. M. Nakao et al., Evaluation of XcalableACC with tightly coupled accelerators/InfiniBand
hybrid communication on accelerated cluster. Int. J. High Perform. Comput. Appl. 33,
109434201882116 (2019)
12. R.W. Numrich et al., Co-array Fortran for parallel programming. SIGPLAN Fortran Forum
17(2), 1–31 (1998)
13. M. Nowicki et al., PCJ - Java library for high performance computing in PGAS model, in
International Conference on High Performance Computing & Simulation (2014), pp. 202–209
14. Y. Zheng et al., UPC++: A PGAS Extension for C++, in IEEE 28th International Parallel and
Distributed Processing Symposium (2014), pp. 1105–1114
15. K. Vivek et al., HabaneroUPC++: a compiler-free PGAS library, in Proceedings of the 8th
International Conference on Partitioned Global Address Space Programming Models, No. 5
(2014), pp. 5:1–5:10
16. C. Philippe et al., X10: an object-oriented approach to non-uniform cluster computing, in
OOPSLA ’05 Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-Oriented
Programming, Systems, Languages, and Applications, vol. 40, No. 10 (2005), pp. 519–538
17. B.L. Chamberlain et al., Parallel programmability and the Chapel language. Int. J. High
Perform. Comput. Appl. 21(3), 291–312 (2007)
M. Nakao
procedure. Since many existing parallel applications are written in MPI library, these
functions can be useful for extending existing applications. Furthermore, it will be
possible to effectively use the high functionality of Python.
References
1. M. Nakao et al., Linkage of XcalableMP and Python languages for high productivity on HPC
cluster system, in Proceedings of Workshops of HPC Asia, No .9 (2018), pp.39–47
2. F. Cantonnet et al., Productivity analysis of the UPC language, in 18th International Parallel
and Distributed Processing Symposium (2004), pp.254–260
3. K. Yelick et al., Productivity and performance using partitioned global address space languages, in Proceedings of the International Workshop on Parallel Symbolic Computation
(2007)
4. A.I. Stone et al., Evaluating coarray Fortran with the CGPOP Miniapp, in Proceedings of the
Fifth Conference on Partitioned Global Address Space Programming Models (2011)
5. M. Nakao et al., Implementation and evaluation of the HPC challenge benchmark in the
XcalableMP PGAS language. Int. J. High Perform. Comput. Appl. 33, 110–123 (2017)
6. J. Jithin et al., Unifying UPC and MPI runtimes: experience with MVAPICH, in Proceedings
of the Fourth Conference on Partitioned Global Address Space Programming Model (2010),
pp. 5:1–5:10
7. M. Nakao et al., Productivity and performance of the HPC challenge benchmarks with the
XcalableMP PGAS language, in Proceedings of the Fourth Conference on Partitioned Global
Address Space Programming Model (2013), pp. 157–171
8. M. Nakao et al., Productivity and performance of global-view programming with XcalableMP
PGAS language, in Proceedings of the 2012 12th IEEE/ACM International Symposium on
Cluster, Cloud and Grid Computing (2012), pp. 402–409
9. M. Nakao et al., XcalableACC: extension of XcalableMP PGAS language using OpenACC for
accelerator clusters, in Proceedings of the First Workshop on Accelerator Programming Using
Directives (2014), pp.27–36
10. M. Nakao et al., Implementing lattice QCD application with XcalableACC language on
accelerated cluster, in IEEE International Conference on Cluster Computing (CLUSTER)
(2017), pp. 429–438
11. M. Nakao et al., Evaluation of XcalableACC with tightly coupled accelerators/InfiniBand
hybrid communication on accelerated cluster. Int. J. High Perform. Comput. Appl. 33,
109434201882116 (2019)
12. R.W. Numrich et al., Co-array Fortran for parallel programming. SIGPLAN Fortran Forum
17(2), 1–31 (1998)
13. M. Nowicki et al., PCJ - Java library for high performance computing in PGAS model, in
International Conference on High Performance Computing & Simulation (2014), pp. 202–209
14. Y. Zheng et al., UPC++: A PGAS Extension for C++, in IEEE 28th International Parallel and
Distributed Processing Symposium (2014), pp. 1105–1114
15. K. Vivek et al., HabaneroUPC++: a compiler-free PGAS library, in Proceedings of the 8th
International Conference on Partitioned Global Address Space Programming Models, No. 5
(2014), pp. 5:1–5:10
16. C. Philippe et al., X10: an object-oriented approach to non-uniform cluster computing, in
OOPSLA ’05 Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-Oriented
Programming, Systems, Languages, and Applications, vol. 40, No. 10 (2005), pp. 519–538
17. B.L. Chamberlain et al., Parallel programmability and the Chapel language. Int. J. High
Perform. Comput. Appl. 21(3), 291–312 (2007)